基于Python爬虫的淘宝服装数据分析项目

文章目录

  • 一.项目介绍
  • 二.爬虫代码
    • 代码分析
  • 三. 数据处理
  • 四. 数据可视化

一.项目介绍

该项目是基于Python爬虫的淘宝服装数据分析项目,以致于帮助商家了解当前服装市场的需求,制定更加精确的营销策略。首先,需要爬取淘宝中关于服装的大量数据,然后对数据进行处理和分析,比如处理重复值,提取关键信息,统计分析等等,最后,通过Echarts将处理后的数据进行可视化展示,可以更为直观的看到服装市场的趋势和变化。

二.爬虫代码

import json
import os
import requests
from selenium import webdriver
from selenium.common.exceptions import TimeoutException
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from pyquery import PyQuery as pq # 作用:解析网页
import time
import openpyxl as opclass TaobaoCrawler:# 初始化def __init__(self, keyword, start_page, end_page):self.keyword = keywordself.start_page = start_pageself.end_page = end_pageself.count = 1self.driver = self.init_driver()self.wait = WebDriverWait(self.driver, 10)self.wb, self.ws = self.init_excel()self.products = []self.create_images_folder()# 创建图片文件夹def create_images_folder(self):if not os.path.exists('images'):os.makedirs('images')# 下载图片def download_image(self, img_url, img_name):if img_url:response = requests.get(img_url)if response.status_code == 200:with open(f'images/{img_name}.png', 'wb') as f:f.write(response.content)else:print(f"Image URL is None for {img_name}, skipping download.")# 初始化浏览器def init_driver(self):options = webdriver.ChromeOptions()options.add_experimental_option("excludeSwitches", ['enable-automation'])driver = webdriver.Chrome(options=options)driver.maximize_window()return driver# 初始化Exceldef init_excel(self):wb = op.Workbook()ws = wb.create_sheet(index=0)title_list = ['Page', 'Num', 'Title', 'Price', 'Deal', 'Location', 'Shop', 'IsPostFree', 'Title_URL', 'Shop_URL', 'Img_URL']for i, title in enumerate(title_list):ws.cell(row=self.count, column=i + 1, value=title)self.count += 1return wb, ws# 搜索商品def search_goods(self):try:# 搜索商品print('正在搜索: ')self.driver.get('https://www.taobao.com')self.driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument",{"source": """Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"""})input = self.wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#q")))# 提交搜索submit = self.wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, '#J_TSearchForm > div.search-button > button')))# 输入关键字input.send_keys(self.keyword)submit.click()# 等待页面加载完成if self.start_page != 1:time.sleep(3)page_input = self.wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/span[3]/input')))page_input.send_keys(self.start_page)admit = self.wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/button[3]')))admit.click()# 获取商品信息self.get_goods(self.start_page)for i in range(self.start_page + 1, self.end_page + 1):self.page_turning(i)# 捕获异常except TimeoutException:print("search_goods: error")self.search_goods()# 翻页def page_turning(self, page_number):print('正在翻页: ', page_number)try:time.sleep(2)submit = self.wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/button[2]')))submit.click()self.wait.until(EC.text_to_be_present_in_element((By.XPATH, '//*[@id="sortBarWrap"]/div[1]/div[2]/div[2]/div[8]/div/span/em'), str(page_number)))self.get_goods(page_number)except TimeoutException:print("page_number: error")self.page_turning(page_number)# 获取商品信息def get_goods(self, page):time.sleep(10)html = self.driver.page_sourcedoc = pq(html)items = doc('div.PageContent--contentWrap--mep7AEm > div.LeftLay--leftWrap--xBQipVc > div.LeftLay--leftContent--AMmPNfB > div.Content--content--sgSCZ12 > div > a').items()for item in items:title = item.find('.Title--title--jCOPvpf span').text()price_int = item.find('.Price--priceInt--ZlsSi_M').text()price_float = item.find('.Price--priceFloat--h2RR0RK').text()price = float(f"{price_int}{price_float}") if price_int and price_float else 0.0deal = item.find('.Price--realSales--FhTZc7U').text()location = item.find('.Price--procity--_7Vt3mX').text()shop = item.find('.ShopInfo--TextAndPic--yH0AZfx a').text()postText = "包邮" if "包邮" in item.find('.SalesPoint--subIconWrapper--s6vanNY span').text() else "/"t_url = item.attr('href')shop_url = item.find('.ShopInfo--TextAndPic--yH0AZfx a').attr('href')img_url = item.find('.MainPic--mainPicWrapper--iv9Yv90 img').attr('src')product = {'Page': page,'Num': self.count - 1,'title': title,'price': price,'deal': deal,'location': location,'shop': shop,'isPostFree': postText,'url': t_url,'shop_url': shop_url,'img_url': img_url}print(product)self.products.append(product)self.ws.cell(row=self.count, column=1, value=page)self.ws.cell(row=self.count, column=2, value=self.count - 1)self.ws.cell(row=self.count, column=3, value=title)self.ws.cell(row=self.count, column=4, value=price)self.ws.cell(row=self.count, column=5, value=deal)self.ws.cell(row=self.count, column=6, value=location)self.ws.cell(row=self.count, column=7, value=shop)self.ws.cell(row=self.count, column=8, value=postText)self.ws.cell(row=self.count, column=9, value=t_url)self.ws.cell(row=self.count, column=10, value=shop_url)self.ws.cell(row=self.count, column=11, value=img_url)self.count += 1self.download_image(img_url, f'img_{self.count - 1}')# 保存数据def save_excel(self, filename):self.wb.save(filename=filename)print(f"{filename} 存储成功~")# 保存jsondef save_json(self, filename):with open(filename, 'w', encoding='utf-8') as f:json.dump(self.products, f, ensure_ascii=False, indent=4)print(f"{filename} 存储成功~")# 运行def run(self):self.search_goods()excel_filename = 'data/服装(From-Tao).xlsx'self.save_excel(excel_filename)json_filename = 'data/content.json'self.save_json(json_filename)self.driver.quit()# 主函数
if __name__ == '__main__':crawler = TaobaoCrawler(keyword='服装', start_page=1, end_page=45)crawler.run()

代码分析

import json
import os
import requests
from selenium import webdriver
from selenium.common.exceptions import TimeoutException
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from pyquery import PyQuery as pq # 作用:解析网页
import time
import openpyxl as op

导入模块

模块作用
json用于处理 JSON 数据。
os用于操作系统相关功能,如文件路径操作。
requests用于发送 HTTP 请求。
selenium 相关模块用于自动化浏览器操作。
pyquery用于解析 HTML 文档。
time用于时间相关操作。
openpyxl用于操作 Excel 文件`
# 主函数
if __name__ == '__main__':crawler = TaobaoCrawler(keyword='服装', start_page=1, end_page=45)crawler.run()

代码首先从主函数进入,传入三个参数,运行TaobaoCrawler爬虫类

# 初始化def __init__(self, keyword, start_page, end_page):self.keyword = keywordself.start_page = start_pageself.end_page = end_pageself.count = 1self.driver = self.init_driver()self.wait = WebDriverWait(self.driver, 10)self.wb, self.ws = self.init_excel()self.products = []self.create_images_folder()# 创建图片文件夹def create_images_folder(self):if not os.path.exists('images'):os.makedirs('images')# 下载图片def download_image(self, img_url, img_name):if img_url:response = requests.get(img_url)if response.status_code == 200:with open(f'images/{img_name}.png', 'wb') as f:f.write(response.content)else:print(f"Image URL is None for {img_name}, skipping download.")# 初始化浏览器def init_driver(self):options = webdriver.ChromeOptions()options.add_experimental_option("excludeSwitches", ['enable-automation'])driver = webdriver.Chrome(options=options)driver.maximize_window()return driver# 初始化Exceldef init_excel(self):wb = op.Workbook()ws = wb.create_sheet(index=0)title_list = ['Page', 'Num', 'Title', 'Price', 'Deal', 'Location', 'Shop', 'IsPostFree', 'Title_URL', 'Shop_URL', 'Img_URL']for i, title in enumerate(title_list):ws.cell(row=self.count, column=i + 1, value=title)self.count += 1return wb, ws

初始化方法 _init

  1. 初始化爬虫的关键字、起始页和结束页。
  2. 初始化浏览器驱动、等待对象、Excel 文件和工作表。
  3. 创建存储图片的文件夹。

方法 create_images_folder: 检查并创建存储图片的文件夹。
方法 download_image: 下载并保存图片到本地。
方法 init_driver: 初始化并配置 Chrome 浏览器驱动。
方法 init_excel: 初始化 Excel 文件和工作表,并设置表头。

# 搜索商品def search_goods(self):try:# 搜索商品print('正在搜索: ')self.driver.get('https://www.taobao.com')self.driver.execute_cdp_cmd("Page.addScriptToEvaluateOnNewDocument",{"source": """Object.defineProperty(navigator, 'webdriver', {get: () => undefined})"""})input = self.wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, "#q")))# 提交搜索submit = self.wait.until(EC.element_to_be_clickable((By.CSS_SELECTOR, '#J_TSearchForm > div.search-button > button')))# 输入关键字input.send_keys(self.keyword)submit.click()# 等待页面加载完成if self.start_page != 1:time.sleep(3)page_input = self.wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/span[3]/input')))page_input.send_keys(self.start_page)admit = self.wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/button[3]')))admit.click()# 获取商品信息self.get_goods(self.start_page)for i in range(self.start_page + 1, self.end_page + 1):self.page_turning(i)# 捕获异常except TimeoutException:print("search_goods: error")self.search_goods()

方法 search_goods:

  1. 打开淘宝主页,输入关键字并搜索商品。
  2. 如果起始页不是第一页,跳转到指定页码。
  3. 获取商品信息。
# 翻页def page_turning(self, page_number):print('正在翻页: ', page_number)try:time.sleep(2)submit = self.wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@id="pageContent"]/div[1]/div[3]/div[4]/div/div/button[2]')))submit.click()self.wait.until(EC.text_to_be_present_in_element((By.XPATH, '//*[@id="sortBarWrap"]/div[1]/div[2]/div[2]/div[8]/div/span/em'), str(page_number)))self.get_goods(page_number)except TimeoutException:print("page_number: error")self.page_turning(page_number)

方法 page_turning: 翻页并获取商品信息

# 获取商品信息def get_goods(self, page):time.sleep(10)html = self.driver.page_sourcedoc = pq(html)items = doc('div.PageContent--contentWrap--mep7AEm > div.LeftLay--leftWrap--xBQipVc > div.LeftLay--leftContent--AMmPNfB > div.Content--content--sgSCZ12 > div > a').items()for item in items:title = item.find('.Title--title--jCOPvpf span').text()price_int = item.find('.Price--priceInt--ZlsSi_M').text()price_float = item.find('.Price--priceFloat--h2RR0RK').text()price = float(f"{price_int}{price_float}") if price_int and price_float else 0.0deal = item.find('.Price--realSales--FhTZc7U').text()location = item.find('.Price--procity--_7Vt3mX').text()shop = item.find('.ShopInfo--TextAndPic--yH0AZfx a').text()postText = "包邮" if "包邮" in item.find('.SalesPoint--subIconWrapper--s6vanNY span').text() else "/"t_url = item.attr('href')shop_url = item.find('.ShopInfo--TextAndPic--yH0AZfx a').attr('href')img_url = item.find('.MainPic--mainPicWrapper--iv9Yv90 img').attr('src')product = {'Page': page,'Num': self.count - 1,'title': title,'price': price,'deal': deal,'location': location,'shop': shop,'isPostFree': postText,'url': t_url,'shop_url': shop_url,'img_url': img_url}print(product)self.products.append(product)self.ws.cell(row=self.count, column=1, value=page)self.ws.cell(row=self.count, column=2, value=self.count - 1)self.ws.cell(row=self.count, column=3, value=title)self.ws.cell(row=self.count, column=4, value=price)self.ws.cell(row=self.count, column=5, value=deal)self.ws.cell(row=self.count, column=6, value=location)self.ws.cell(row=self.count, column=7, value=shop)self.ws.cell(row=self.count, column=8, value=postText)self.ws.cell(row=self.count, column=9, value=t_url)self.ws.cell(row=self.count, column=10, value=shop_url)self.ws.cell(row=self.count, column=11, value=img_url)self.count += 1self.download_image(img_url, f'img_{self.count - 1}')

方法 get_goods: 解析当前页面的商品信息并保存到 Excel 和 JSON 文件中。

# 保存数据def save_excel(self, filename):self.wb.save(filename=filename)print(f"{filename} 存储成功~")# 保存jsondef save_json(self, filename):with open(filename, 'w', encoding='utf-8') as f:json.dump(self.products, f, ensure_ascii=False, indent=4)print(f"{filename} 存储成功~")# 运行def run(self):self.search_goods()excel_filename = 'data/服装(From-Tao).xlsx'self.save_excel(excel_filename)json_filename = 'data/content.json'self.save_json(json_filename)self.driver.quit()

方法 save_excel: 保存 Excel 文件。
方法 save_json: 保存 JSON 文件。
方法 run: 执行爬虫任务,保存数据并关闭浏览器。

三. 数据处理

const fs = require('fs').promises;
const path = require('path');let data = null;
let d = null;// 定义一个异步函数来加载数据
async function loadData() {try {const filePath = path.join(__dirname, '../data', 'content.json');const fileContent = await fs.readFile(filePath, 'utf8');data = JSON.parse(fileContent);console.log('Data loaded successfully:', data);} catch (error) {console.error('Failed to load data:', error);}
}// 在程序启动时调用loadData函数
loadData().then(() => {// if (data) {//     console.log('Global data:', data[0].title);// }console.log("data.length", data.length)const dict = data.reduce((acc, item) => {acc[item.title] = itemreturn acc}, {})// console.log("dict", dict)const dict_arr = []for (const title in dict) {dict_arr.push(dict[title])}
// console.log("arr", arr)console.log("dict_arr.length", dict_arr.length)d = dict_arr.reduce((acc, item) => {let flag = falsefor (const v of acc) {if (v.name === item.location) {v.value += 1flag = true}}if (flag === false) {acc.push({name: item.location,value: 1})}return acc}, [])// 按value值降序排序d.sort((a, b) => b.value - a.value);console.log("data_handle\n", d)}).catch(error => {// 处理加载数据时的错误console.error('Failed to load data during startup:', error);
});
  1. 导入模块:
    fs:用于文件系统操作,使用 promises 以便使用异步函数。
    path:用于处理和转换文件路径。
  2. 变量声明:
    data:用于存储从 JSON 文件加载的数据。
    d:用于存储处理后的数据。
  3. 函数 loadData:
    1. 异步函数,用于加载 JSON 文件中的数据。
    2. 使用 fs.readFile 读取文件内容,并将其解析为 JSON 对象。
      1. 如果读取成功,数据将存储在 data 变量中,并在控制台输出成功消息。
      2. 如果读取失败,捕获错误并在控制台输出错误消息。
  4. 数据处理:
    1. 在程序启动时调用 loadData 函数,并在数据加载完成后进行处理。
    2. 使用 reduce 方法将数据转换为字典 dict,其中键为商品标题,值为商品对象。
    3. 将字典转换为数组 dict_arr,以便进一步处理。
    4. 使用 reduce 方法将 dict_arr 转换为按 location 分组的数组 d,并统计每个 location 的商品数量。
    5. 按 value 值对 d 进行降序排序。
    6. 在控制台输出处理后的数据。

四. 数据可视化

<!DOCTYPE html>
<html lang="en" style="height: 100%">
<head><meta charset="utf-8"><title>Region Distribution Map</title><style>body, html {height: 100%;margin: 0;font-family: Arial, sans-serif;background-color: #f4f4f4;}#container {height: 100%;width: 100%;max-width: 1200px;margin: 0 auto;padding: 20px;box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);background-color: #fff;border-radius: 8px;}</style>
</head>
<body>
<div id="container"></div><script src="./js/echarts.min.js"></script>
<script src="./js/china.js"></script>
<script>document.addEventListener('DOMContentLoaded', () => {const dom = document.getElementById('container');const myChart = echarts.init(dom, null, {renderer: 'canvas',useDirtyRect: false});const data = [{name: '福建', value: 716},{name: '浙江', value: 557},{name: '广东', value: 340},{name: '湖北', value: 206},{name: '江苏', value: 67},{name: '上海', value: 12},{name: '安徽', value: 10},{name: '北京', value: 7},{name: '山东', value: 4},{name: '江西', value: 3},{name: '湖南', value: 3},{name: '广西', value: 3},{name: '河南', value: 3},{name: '河北', value: 1},{name: '四川', value: 1},{name: '新疆', value: 1},{name: '辽宁', value: 1}];const option = {title: {text: '中国服装发出地',left: 'center',textStyle: {color: '#333',fontSize: 24}},tooltip: {trigger: 'item',formatter: '{b}: {c}'},visualMap: {min: 0,max: 700,left: 'left',top: 'bottom',text: ['High', 'Low'],calculable: true,inRange: {color: ['#e0ffff', '#006edd']}},series: [{name: '服装发出地',type: 'map',map: 'china',roam: true,label: {show: true},data: data}]};myChart.setOption(option);window.addEventListener('resize', myChart.resize);});
</script>
</body>
</html>

效果展示:
20240907153000

中间需要俩个js文件:

  1. china.js
(function (root, factory) {if (typeof define === 'function' && define.amd) {// AMD. Register as an anonymous module.define(['exports', 'echarts'], factory);} else if (typeof exports === 'object' && typeof exports.nodeName !== 'string') {// CommonJSfactory(exports, require('echarts'));} else {// Browser globalsfactory({}, root.echarts);}
}(this, function (exports, echarts) {var log = function (msg) {if (typeof console !== 'undefined') {console && console.error && console.error(msg);}}if (!echarts) {log('ECharts is not Loaded');return;}if (!echarts.registerMap) {log('ECharts Map is not loaded')return;}echarts.registerMap('china', {"type":"FeatureCollection","features":[{"id":"710000","type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[["@@°Ü¯Û"],["@@ƛĴÕƊÉɼģºðʀ\\ƎsÆNŌÔĚäœnÜƤɊĂǀĆĴžĤNJŨxĚĮǂƺòƌ‚–âÔ®ĮXŦţƸZûЋƕƑGđ¨ĭMó·ęcëƝɉlÝƯֹÅŃ^Ó·śŃNjƏďíåɛGɉ™¿@ăƑŽ¥ĘWǬÏĶŁâ"],["@@\\p|WoYG¿¥I†j@¢"],["@@
¡‰@ˆV^RqˆBbAŒnTXeRz¤Lž«³I"],["@@ÆEE—„kWqë @œ"],["@@fced"]],"encodeOffsets":[[[122886,24033]],[[123335,22980]],[[122375,24193]],[[122518,24117]],[[124427,22618]],[[124862,26043]]]},"properties":{"cp":[121.509062,25.044332],"name":"台湾","childNum":6}},{"id":"130000","type":"Feature","geometry":{"type":"MultiPolygon","coordinates":[["@@o~Z]‚ªr‰ºc_ħ²G¼s`jΟnüsÂœłNX_“M`ǽÓnUK
Ĝēs¤­©yrý§uģŒc†JŠ›e"],["@@U`Ts¿m‚"],["@@oºƋÄd–eVŽDJj£€J|Ådz•Ft~žKŨ¸IÆv|”‡¢r}膎onb˜}`RÎÄn°ÒdÞ²„^®’lnÐèĄlðÓœ×]ªÆ}LiĂ±Ö`^°Ç¶p®đDcœŋ`–ZÔ’¶êqvFÆš†N®ĆTH®¦O’¾ŠIbÐã´BĐɢŴÆíȦp–ĐÞXR€·nndOž¤’OÀĈƒ­Qg˜µFo|gȒęSWb©osx|hYh•gŃfmÖĩnº€TÌ’Sp›¢dYĤ¶UĈjl’ǐpäìë|³kÛfw²Xjz~ÂqbTŠÑ„ěŨ@|oM‡’zv¢ZrÃVw¬ŧĖ¸fŒ°ÐT€ªqŽs{Sž¯r æÝlNd®²Ğ džiGĘ‚J™¼lr}~K¨ŸƐÌWö€™ÆŠzRš¤lêmĞL΄’@¡|q]SvK€ÑcwpÏρ†ĿćènĪWlĄkT}ˆJ”¤~ƒÈT„d„™pddʾĬŠ”ŽBVt„EÀ¢ôPĎƗè@~‚k–ü\\rÊĔÖæW_§¼F˜†´©òDòj’ˆYÈrbĞāøŀG{ƀ|¦ðrb|ÀH`pʞkv‚GpuARhÞÆǶgĘŠTǼƹS£¨¡ù³ŘÍ]¿Ây™ôEP xX¶¹܇O¡“gÚ¡IwÃ鑦ÅB‡Ï|Ç°
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  1. echarts.min.js
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<html lang="en" style="height: 100%">
<head><meta charset="utf-8"><title>Clothing Category Distribution</title><style>body {font-family: Arial, sans-serif;margin: 0;padding: 0;display: flex;justify-content: center;align-items: center;height: 100%;background-color: #f4f4f4;}#container {width: 80%;height: 80%;}</style>
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<body><div id="container"></div><script type="text/javascript" src="https://registry.npmmirror.com/echarts/5.5.1/files/dist/echarts.min.js"></script><script type="text/javascript">var dom = document.getElementById('container');var myChart = echarts.init(dom, null, {renderer: 'canvas',useDirtyRect: false});var app = {};var data = [{ name: '外套', value: 377 },{ name: '衬衫', value: 170 },{ name: '长袖', value: 200 },{ name: '卫衣', value: 57 },{ name: 'T恤', value: 108 },{ name: '内衣', value: 8 },{ name: '裤子', value: 734 },{ name: '裙子', value: 150 },{ name: '运动装', value: 805 }];// Sort data in descending orderdata.sort((a, b) => b.value - a.value);var option = {title: {text: 'Clothing Category Distribution',subtext: 'Based on the number of items',left: 'center',textStyle: {color: '#333',fontSize: 24},subtextStyle: {color: '#666',fontSize: 14}},tooltip: {trigger: 'axis',axisPointer: {type: 'shadow'},formatter: '{b}: {c} items'},grid: {left: '3%',right: '4%',bottom: '3%',containLabel: true},xAxis: {type: 'category',data: data.map(item => item.name),axisTick: {alignWithLabel: true},axisLabel: {interval: 0,rotate: 30,textStyle: {fontSize: 12}}},yAxis: {type: 'value',axisLabel: {textStyle: {fontSize: 12}},splitLine: {show: true,lineStyle: {color: '#ddd'}}},series: [{name: '数量',type: 'bar',barWidth: '60%',data: data.map(item => item.value),itemStyle: {color: new echarts.graphic.LinearGradient(0, 0, 0, 1, [{ offset: 0, color: '#83bff6' },{ offset: 0.5, color: '#188df0' },{ offset: 1, color: '#188df0' }])},showBackground: true,backgroundStyle: {color: 'rgba(180, 180, 180, 0.2)'},label: {show: true,position: 'top',formatter: '{c}'}}]};if (option && typeof option === 'object') {myChart.setOption(option);}window.addEventListener('resize', myChart.resize);</script>
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效果展示:
20240907153225

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